826 lines
34 KiB
Python
826 lines
34 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only DeepseekV2/DeepseekV3 model."""
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from typing import Any, Dict, Iterable, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, ModelConfig, VllmConfig
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from vllm.distributed import (get_pp_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP
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from .utils import (PPMissingLayer, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class DeepseekV2MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj")
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj")
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class DeepseekV2MoE(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.routed_scaling_factor = config.routed_scaling_factor
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self.n_shared_experts = config.n_shared_experts
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if config.hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now.")
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self.gate = ReplicatedLinear(config.hidden_size,
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config.n_routed_experts,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.gate")
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if config.topk_method == "noaux_tc":
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.empty(config.n_routed_experts))
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else:
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self.gate.e_score_correction_bias = None
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self.experts = FusedMoE(
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num_experts=config.n_routed_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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prefix=f"{prefix}.experts",
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scoring_func=config.scoring_func,
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e_score_correction_bias=self.gate.e_score_correction_bias)
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if config.n_shared_experts is not None:
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intermediate_size = (config.moe_intermediate_size *
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config.n_shared_experts)
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self.shared_experts = DeepseekV2MLP(
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.shared_experts",
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.n_shared_experts is not None:
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shared_output = self.shared_experts(hidden_states)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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if hidden_states.dtype != torch.float16:
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits) * self.routed_scaling_factor
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else:
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# This is a special case to avoid FP16 overflow
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final_hidden_states = self.experts(hidden_states=hidden_states,
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router_logits=router_logits)
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if shared_output is not None:
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if hidden_states.dtype != torch.float16:
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final_hidden_states = final_hidden_states + shared_output
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else:
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# This is a special case to avoid FP16 overflow
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final_hidden_states = final_hidden_states + shared_output \
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* (1. / self.routed_scaling_factor)
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
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import math
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if scale <= 1:
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return 1.0
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return 0.1 * mscale * math.log(scale) + 1.0
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class DeepseekV2Attention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int,
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert num_heads % tp_size == 0
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self.num_local_heads = num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_a_proj")
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self.q_a_layernorm = RMSNorm(self.q_lora_rank,
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eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(q_lora_rank,
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self.num_heads *
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self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_b_proj")
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else:
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self.q_proj = ColumnParallelLinear(self.hidden_size,
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self.num_heads *
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self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj")
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_a_proj_with_mqa")
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
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eps=config.rms_norm_eps)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_b_proj")
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# O projection.
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self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj")
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if rope_scaling:
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rope_scaling["rope_type"] = 'deepseek_yarn'
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self.rotary_emb = get_rope(qk_rope_head_dim,
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rotary_dim=qk_rope_head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=False)
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if rope_scaling:
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mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
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scaling_factor = rope_scaling["factor"]
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mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
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self.scaling = self.scaling * mscale * mscale
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self.attn = Attention(self.num_local_heads,
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self.qk_head_dim,
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self.scaling,
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num_kv_heads=self.num_local_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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if self.q_lora_rank is not None:
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q = self.q_a_proj(hidden_states)[0]
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q = self.q_a_layernorm(q)
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q = self.q_b_proj(q)[0].view(-1, self.num_local_heads,
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self.qk_head_dim)
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else:
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q = self.q_proj(hidden_states)[0].view(-1, self.num_local_heads,
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self.qk_head_dim)
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q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
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dim=-1)
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latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
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kv_a, _ = latent_cache.split(
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[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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latent_cache = latent_cache.unsqueeze(1)
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kv_a = self.kv_a_layernorm(kv_a.contiguous())
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kv = self.kv_b_proj(kv_a)[0]
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kv = kv.view(-1, self.num_local_heads,
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self.qk_nope_head_dim + self.v_head_dim)
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k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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k_pe = latent_cache[:, :, self.kv_lora_rank:]
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q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
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q[..., self.qk_nope_head_dim:] = q_pe
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k = torch.empty_like(q)
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k[..., :self.qk_nope_head_dim] = k_nope
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k[..., self.qk_nope_head_dim:] = k_pe
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# padding value to qk_head_dim for alignment
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v = torch.nn.functional.pad(
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v, [0, self.qk_head_dim - self.v_head_dim],
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value=0).view(-1, self.num_local_heads * self.qk_head_dim)
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attn_output = self.attn(q, k, v)
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attn_output = attn_output.view(
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-1, self.num_local_heads,
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self.qk_head_dim)[..., :self.v_head_dim].reshape(
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-1, self.num_local_heads * self.v_head_dim)
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output, _ = self.o_proj(attn_output)
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return output
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class DeepseekV2MLAAttention(nn.Module):
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"""
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Main reference: DeepseekV2 paper, and FlashInfer Implementation
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(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
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For more info see MLACommonImpl in: vllm/attention/backends/mla/utils.py
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: Optional[int],
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert num_heads % tp_size == 0
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self.num_local_heads = num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_a_proj")
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self.q_a_layernorm = RMSNorm(self.q_lora_rank,
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eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(q_lora_rank,
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self.num_heads *
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self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_b_proj")
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else:
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self.q_proj = ColumnParallelLinear(self.hidden_size,
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self.num_heads *
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self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj")
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_a_proj_with_mqa")
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
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eps=config.rms_norm_eps)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_b_proj")
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self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj")
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if rope_scaling:
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rope_scaling["rope_type"] = 'deepseek_yarn'
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self.rotary_emb = get_rope(qk_rope_head_dim,
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rotary_dim=qk_rope_head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=False)
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if rope_scaling:
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mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
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scaling_factor = rope_scaling["factor"]
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mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
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self.scaling = self.scaling * mscale * mscale
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# In the MLA backend, kv_cache includes both k_c and
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# pe (i.e. decoupled position embeddings). In particular,
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# the concat_and_cache_mla op requires
|
|
# k_c.size(1) + k_pe.size(1) == kv_cache.size(2)
|
|
# i.e.
|
|
# kv_lora_rank + qk_rope_head_dim == head_size
|
|
self.mla_attn = Attention(
|
|
num_heads=self.num_local_heads,
|
|
head_size=self.kv_lora_rank + self.qk_rope_head_dim,
|
|
scale=self.scaling,
|
|
num_kv_heads=1,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
use_mla=True,
|
|
# MLA Args
|
|
q_lora_rank=self.q_lora_rank,
|
|
kv_lora_rank=self.kv_lora_rank,
|
|
qk_nope_head_dim=self.qk_nope_head_dim,
|
|
qk_rope_head_dim=self.qk_rope_head_dim,
|
|
qk_head_dim=self.qk_head_dim,
|
|
v_head_dim=self.v_head_dim,
|
|
rotary_emb=self.rotary_emb,
|
|
q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
|
|
kv_b_proj=self.kv_b_proj,
|
|
o_proj=self.o_proj,
|
|
)
|
|
|
|
self.prefix = prefix
|
|
self.debug_layer_idx = int(self.prefix.split(".")[-2])
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
if self.q_lora_rank is not None:
|
|
ckq = self.q_a_proj(hidden_states)[0]
|
|
hidden_states_or_q_c = self.q_a_layernorm(ckq)
|
|
else:
|
|
hidden_states_or_q_c = hidden_states
|
|
kv_c, k_pe = self.kv_a_proj_with_mqa(hidden_states)[0].split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
|
|
return self.mla_attn(hidden_states_or_q_c,
|
|
kv_c_normed,
|
|
k_pe,
|
|
output_shape=hidden_states.shape)
|
|
|
|
|
|
class DeepseekV2DecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
prefix: str,
|
|
model_config: ModelConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
max_position_embeddings = getattr(config, "max_position_embeddings",
|
|
8192)
|
|
# DecoderLayers are created with `make_layers` which passes the prefix
|
|
# with the layer's index.
|
|
layer_idx = int(prefix.split(sep='.')[-1])
|
|
if model_config.use_mla:
|
|
attn_cls = DeepseekV2MLAAttention
|
|
else:
|
|
attn_cls = DeepseekV2Attention
|
|
self.self_attn = attn_cls(
|
|
config=config,
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
qk_nope_head_dim=config.qk_nope_head_dim,
|
|
qk_rope_head_dim=config.qk_rope_head_dim,
|
|
v_head_dim=config.v_head_dim,
|
|
q_lora_rank=config.q_lora_rank
|
|
if hasattr(config, "q_lora_rank") else None,
|
|
kv_lora_rank=config.kv_lora_rank,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.self_attn",
|
|
)
|
|
|
|
if (config.n_routed_experts is not None
|
|
and layer_idx >= config.first_k_dense_replace
|
|
and layer_idx % config.moe_layer_freq == 0):
|
|
self.mlp = DeepseekV2MoE(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
else:
|
|
self.mlp = DeepseekV2MLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
self.input_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
self.routed_scaling_factor = config.routed_scaling_factor
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
residual: Optional[torch.Tensor],
|
|
) -> torch.Tensor:
|
|
# Self Attention
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(
|
|
hidden_states, residual)
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
)
|
|
|
|
# Fully Connected
|
|
if isinstance(self.mlp, DeepseekV2MoE) and \
|
|
hidden_states.dtype == torch.float16:
|
|
# This is a special case to avoid FP16 overflow
|
|
hidden_states *= 1. / self.routed_scaling_factor
|
|
hidden_states, residual = self.post_attention_layernorm(
|
|
hidden_states, residual)
|
|
hidden_states = self.mlp(hidden_states)
|
|
if isinstance(self.mlp, DeepseekV2MLP) and \
|
|
hidden_states.dtype == torch.float16:
|
|
# This is a special case to avoid FP16 overflow
|
|
hidden_states *= 1. / self.routed_scaling_factor
|
|
residual *= 1. / self.routed_scaling_factor
|
|
return hidden_states, residual
|
|
|
|
|
|
@support_torch_compile
|
|
class DeepseekV2Model(nn.Module):
|
|
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.vocab_size = config.vocab_size
|
|
|
|
if get_pp_group().is_first_rank:
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.embed_tokens")
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: DeepseekV2DecoderLayer(
|
|
config,
|
|
prefix,
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
),
|
|
prefix=f"{prefix}.layers")
|
|
|
|
if get_pp_group().is_last_rank:
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
self.make_empty_intermediate_tensors = (
|
|
make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size))
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors],
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.get_input_embeddings(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
for layer in self.layers[self.start_layer:self.end_layer]:
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class DeepseekV2ForCausalLM(nn.Module, SupportsPP):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = DeepseekV2Model(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.sampler = get_sampler()
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: Optional[torch.Tensor],
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def make_empty_intermediate_tensors(
|
|
self, batch_size: int, dtype: torch.dtype,
|
|
device: torch.device) -> IntermediateTensors:
|
|
return IntermediateTensors({
|
|
"hidden_states":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
"residual":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
})
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
|
if spec_layer is not None:
|
|
continue # skip spec decode layers for main model
|
|
|
|
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if (("mlp.experts." in name) and name not in params_dict):
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
|
|
pass
|
|
|
|
|
|
def get_spec_layer_idx_from_weight_name(config: PretrainedConfig,
|
|
weight_name: str) -> Optional[int]:
|
|
if hasattr(config,
|
|
"num_nextn_predict_layers") and (config.num_nextn_predict_layers
|
|
> 0):
|
|
layer_idx = config.num_hidden_layers
|
|
for i in range(config.num_nextn_predict_layers):
|
|
if weight_name.startswith(f"model.layers.{layer_idx+i}."):
|
|
return layer_idx + i
|
|
return None
|